AI Governance Policy Builder

Framework to establish AI governance, assess AI maturity, manage algorithmic risks, conduct impact assessments, classify AI system risk, and ensure regulator...

Build internal AI governance policies from scratch. Covers acceptable use, model selection, data handling, vendor contracts, compliance mapping, and board reporting.

When to Use

• Writing or reviewing internal AI acceptable use policies
• Establishing AI governance committees or review boards
• Mapping AI usage to regulatory frameworks (EU AI Act, NIST, ISO 42001)
• Evaluating vendor AI terms and liability clauses
• Preparing board-level AI governance reports

Governance Policy Framework

1. Acceptable Use Policy (AUP)

Every organization running AI needs a written AUP covering:

Permitted Uses

• List approved AI tools by department and function
• Define data classification tiers (public, internal, confidential, restricted)
• Map which data tiers can enter which AI systems
• Specify approved vendors vs. shadow AI (employees using personal ChatGPT accounts)

Prohibited Uses

• Customer PII in non-SOC2 models without anonymization
• Autonomous financial decisions above $[threshold] without human review
• HR screening/scoring without bias audit documentation
• Any use violating sector regulations (HIPAA, GDPR, SOX, PCI-DSS)

Shadow AI Detection

Signal	Risk Level	Action
API calls to unknown AI endpoints	HIGH	Block + investigate
Browser extensions with AI features	MEDIUM	Audit + approve/deny
Personal accounts on company devices	MEDIUM	Policy reminder + monitor
Exported data to AI training sets	CRITICAL	Immediate review

2. AI Model Selection & Procurement

Evaluation Scorecard (100 points)

Criteria	Weight	What to Check
Data residency & sovereignty	20	Where is data processed? Stored? Can you choose region?
Security certifications	20	SOC2 Type II, ISO 27001, HIPAA BAA, FedRAMP
Model transparency	15	Training data provenance, bias testing, version control
Contract terms	15	Data usage rights, indemnification, SLA, exit clauses
Performance & cost	15	Latency, accuracy benchmarks, token pricing, rate limits
Integration & support	15	API stability, documentation quality, support SLA

Minimum score for production deployment: 70/100

Red Flags (automatic disqualification):

• Vendor trains on your data without opt-out
• No data processing agreement (DPA) available
• Indemnification excluded for AI outputs
• No incident response SLA

3. Data Handling & Classification

AI Data Flow Audit Template

For each AI integration, document:

1. Input data: What goes in? Classification tier? PII present?
2. Processing: Where? Which model? Hosted or API? Region?
3. Output data: What comes out? Stored where? Retention period?
4. Training: Does vendor use your data for training? Opt-out confirmed?
5. Logging: Are prompts/responses logged? Where? Who has access?
6. Deletion: Can you request data deletion? Verified how?

Data Minimization Checklist

• Only send minimum necessary data to AI systems
• Strip PII before processing where possible
• Use synthetic data for testing and development
• Implement input sanitization for prompt injection prevention
• Audit output for data leakage (model regurgitating training data)

4. Regulatory Compliance Mapping

EU AI Act (effective Aug 2025, enforcement Feb 2025)

Risk Category	Examples	Requirements
Unacceptable	Social scoring, real-time biometric ID (most cases)	Banned
High-risk	HR screening, credit scoring, medical devices	Conformity assessment, human oversight, transparency
Limited	Chatbots, deepfakes	Transparency obligations (disclose AI use)
Minimal	Spam filters, game AI	No requirements

NIST AI RMF (Risk Management Framework)

• Map: Identify AI systems in use
• Measure: Quantify risks per system
• Manage: Implement controls proportional to risk
• Govern: Establish oversight structure and accountability

ISO 42001 (AI Management System)

• Useful for organizations wanting certified AI governance
• Aligns with ISO 27001 (already have it? Easier path)
• Covers: AI policy, risk assessment, objectives, competence, documentation

5. AI Governance Committee Structure

Recommended Composition

• Chair: CTO or Chief AI Officer
• Legal: 1 representative (contracts, compliance)
• Security: CISO or delegate (data protection, incident response)
• Business: 1-2 department heads (use case prioritization)
• Ethics: External advisor or designated internal role
• Finance: CFO delegate (budget, ROI tracking)

Meeting Cadence

• Monthly: Review new AI use cases, vendor changes, incidents
• Quarterly: Policy updates, compliance audit, budget review
• Annually: Full governance framework review, board report

Decision Authority

Decision	Authority Level
New AI tool (< $5K/year)	Department head + security review
New AI tool (> $5K/year)	Governance committee approval
Customer-facing AI	Committee + legal + CEO sign-off
AI incident response	Security lead (immediate) → Committee (48h review)

6. Vendor Contract Checklist

Before signing any AI vendor contract, confirm:

• Data processing agreement (DPA) signed
• Your data is NOT used for model training (or explicit opt-out confirmed)
• Data residency requirements met (specify regions)
• Indemnification clause covers AI-generated output liability
• SLA includes uptime, latency, and support response time
• Exit clause: data export format, deletion timeline, transition support
• Security certifications current and verified (not expired)
• Incident notification timeline specified (72h or less)
• Subprocessor list provided with change notification rights
• Insurance coverage for AI-specific risks confirmed
• Price lock or cap on increases for contract duration
• Right to audit (or audit report access)

7. Board Reporting Template

Quarterly AI Governance Report

AI GOVERNANCE REPORT — Q[X] [YEAR]

1. AI PORTFOLIO SUMMARY
   - Active AI systems: [count]
   - New deployments this quarter: [count]
   - Retired/replaced: [count]
   - Total AI spend: $[amount] (vs budget: $[amount])

2. RISK DASHBOARD
   - High-risk systems: [count] — all compliant: [Y/N]
   - Open incidents: [count] — resolved this quarter: [count]
   - Shadow AI detections: [count] — remediated: [count]
   - Compliance gaps: [list]

3. VALUE DELIVERED
   - Hours saved: [estimate]
   - Revenue attributed to AI: $[amount]
   - Cost reduction: $[amount]
   - Customer satisfaction impact: [metric]

4. KEY DECISIONS NEEDED
   - [Decision 1: context + recommendation]
   - [Decision 2: context + recommendation]

5. NEXT QUARTER PRIORITIES
   - [Priority 1]
   - [Priority 2]

8. Incident Response for AI Systems

AI-Specific Incident Categories

Category	Example	Response Time
Data breach via AI	Model leaks PII in output	Immediate — invoke security IR plan
Hallucination causing harm	Wrong medical/legal/financial advice acted on	4h — document, notify affected parties
Bias detected	Discriminatory output in hiring/lending	24h — suspend system, audit, remediate
Prompt injection	Attacker manipulates AI behavior	Immediate — block vector, patch
Cost overrun	Runaway API calls	4h — rate limit, investigate, cap
Vendor incident	Provider breach or outage	Per vendor SLA — activate backup

Post-Incident Review Template

1. What happened (factual timeline)
2. Impact (who/what affected, cost, duration)
3. Root cause (not blame — systems thinking)
4. Fixes applied (immediate + permanent)
5. Policy/process changes needed
6. Board notification required? (Y/N + rationale)

Cost of NOT Having AI Governance

Company Size	Annual Risk Without Governance
15-50 employees	$50K-$200K (shadow AI waste, compliance fines)
50-200 employees	$200K-$800K (data incidents, vendor lock-in, redundant tools)
200-1000 employees	$800K-$3M (regulatory penalties, IP exposure, audit failures)
1000+ employees	$3M-$15M+ (class action, regulatory enforcement, reputational damage)

90-Day Implementation Roadmap

Month 1: Foundation

• Draft acceptable use policy
• Inventory all AI systems in use (including shadow AI)
• Classify data flowing through each system
• Identify governance committee members

Month 2: Controls

• Finalize and distribute AUP
• Implement vendor evaluation scorecard for new purchases
• Set up AI incident response procedures
• Begin regulatory compliance mapping

Month 3: Operationalize

• First governance committee meeting
• Deliver first board report
• Establish monitoring for shadow AI
• Schedule quarterly policy review cycle

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